The control of interconnects resistance, intra- and inter-capacitance (RC) variations in order to meet the stringent automotive sigma requirements is an extremely challenging task in the advanced semiconductor manufac...
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ISBN:
(纸本)9781538665091;9781538665084
The control of interconnects resistance, intra- and inter-capacitance (RC) variations in order to meet the stringent automotive sigma requirements is an extremely challenging task in the advanced semiconductor manufacturing due to many process variables to control in the dual damascene integration scheme. We demonstrate that the key interconnects parametric are able to meet Cp/Cpk >1.67 with a combination of real-time in-line automatic processcontrol (APC) of trench critical dimension (CD), trench depth, and copper Chemical Mechanical Polish (CMP) that are well correlated to the parametric performance. We explain how the APC is modeled using feed-forward and feed-backward data to obtain optimum processing conditions for automotive manufacturing by using stochastic in-line data for analysis and control of subsequent upstream and downstream process. Employing variance component analysis, we show that the RC's overall sigma can be improved. Further, the bivariate analysis is shown for parametric output to check on centering to ensure the technology response of interconnect model is maintained as designed.
Noising image recognition is an important but challenging task. The images collected by the camera are often accompanied by a variety of noise, which will cause the loss of image information to make recognition diffic...
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ISBN:
(纸本)9789881563903
Noising image recognition is an important but challenging task. The images collected by the camera are often accompanied by a variety of noise, which will cause the loss of image information to make recognition difficult. With powerful feature extraction, neural network models can he used to process these complex noising images. However, when the noise model to be learned is complex and the number of learning samples is limited, this may lead to overfitting of the network model. In order to increase the diversity of noising images in limited learning samples and improve the generalization ability of the recognition model, we propose a noise modeling method based on CVAE-GAN. First, the encoder maps a real noising image to hidden layer vectors, which contains noise distribution, image category, and pose information. Then the decoder receives these hidden layer vectors to generate an synthetic image of the specified category and noise level. Finally, during the training process, we combine adversarial loss and smoothl1 loss to make the generated noising image more realistic. Experiments show that compared with other current generative models, our method can more effectively learn the distribution rules of complex noise in real images and generate synthetic images more similar to real noise. These synthetic images generated by our method can can alleviate the overfilling problem caused by insufficient data volume and enhance the generalization ability of image recognition models. By this way, we obtain the state-of-the-art recognition performance on the challenging noising image dataset.
From a handling-qualities standpoint, it can be argued that the Wright brothers' 1902 glider represents their most significant design breakthrough on the basis that their subsequent aircraft retained the same basi...
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From a handling-qualities standpoint, it can be argued that the Wright brothers' 1902 glider represents their most significant design breakthrough on the basis that their subsequent aircraft retained the same basic control concept. This paper is a study of that aircraft, and results from wind-tunnel tests' modeling, and flight simulation trials are presented. Unstable in both longitudinal and lateral axes, the 1902 glider's flight dynamics provide maneuverability but also a tendency to pilot-induced oscillation in tight tracking tasks. The Wrights taught themselves to fly on this aircraft and paved the way for the development of their powered flyer. This paper stands as a celebration of the achievements,made by the Wrights 101 years ago and demonstrates the technological leaps they made in the process of designing, building, and flight testing the 1902 glider. The results are placed in the context of modern handling-qualities engineering.
The advent of spatial information technologies, such as GIS, GPS and Remote Sensing, have great enhanced our capabilities to collect and capture spatial data. How to discover potentially useful information and knowled...
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ISBN:
(纸本)9781424408177
The advent of spatial information technologies, such as GIS, GPS and Remote Sensing, have great enhanced our capabilities to collect and capture spatial data. How to discover potentially useful information and knowledge from massive amounts of spatial data is becoming a crucial project for spatial analysis and spatial decision making. Bayesian networks have a powerful ability for reasoning and semantic representation, which combined with qualitative analysis and quantitative analysis, with prior knowledge and observed data, and provides an effective way to spatial data mining. This paper focuses on construction and learning a Bayesian network model for spatial data mining. Firstly, we introduce the theory of spatial data mining and discuss the characteristics of Bayesian networks. A framework and process of spatial data mining is proposed. Then we construct a Bayesian network model for spatial data mining with the given dataset. The experimental results demonstrate the feasibility and practical of the proposed approach to spatial data mining. Finally, we draw a conclusion and show further avenues for research.
Bearings play an important role in mechanical systems, and it is of great significance to monitor the health status of the bearing in real time for ensuring long-term stable operation of mechanical system. This paper ...
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ISBN:
(纸本)9789881563972
Bearings play an important role in mechanical systems, and it is of great significance to monitor the health status of the bearing in real time for ensuring long-term stable operation of mechanical system. This paper proposes a new reconstruction-based fault prognosis method to process the time domain and frequency domain characteristics of vibration signals. Firstly, the principal component analysis (PCA) model is established to detect the fault factors in current measurement data. And then, the fault directions are selected according to its fault correlation and the robustness of the corresponding fault magnitude evolution process. Finally, the vector autocorrelation (VAR) model is used to predict the fault degradation process to achieve complete fault prognosis. The effectiveness of the developed method is validated by PRONOSTIA data.
In this paper, we present a concept of interval granular fuzzy models and elaborate on their detailed development scheme and performance evaluation. The crux of the underlying design is that the information granules a...
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In this paper, we present a concept of interval granular fuzzy models and elaborate on their detailed development scheme and performance evaluation. The crux of the underlying design is that the information granules and information granularity which play a pivotal role in human cognitive and decision-making activities are incorporated into existing fuzzy modeling methods to realize system modeling at the level of information granules. The overall development process of interval granular fuzzy model includes two stages. At the first stage, we form input-error data by computing residual error resulting from the well-established numeric model and then establish granular structures positioned in the input space by clustering input-error data and invoking the principle of justifiable information granularity for weighted data. These granular structures discovered in the input space become condition parts of the rules of the developed interval granular fuzzy model. At the second stage, the interval information granules positioned in the error space are directly induced with the aid of granular structures discovered in the input space. These interval information granules describe the range of residual error produced by the numeric model and are exploited to form the conclusion part of the corresponding rules of the developed interval granular fuzzy model. So far, the interval granular fuzzy model is completely constructed, whose rules help compensate the discrepancies of the numeric model. The output of the developed model is an interval information granule showing a range of possible residual error produced by the numeric model. The several performance indices are presented to evaluate the developed interval granular fuzzy model. A series of numeric experiments completed for synthetic data and real-world data coming from the machine learning repository provide a useful insight into the effectiveness of the presented development scheme, reveal the impact of some parameters
A modeling approach of incorporating SPICE into FDTD code for analysis of microwave active circuits is presented, and a set of communication agreement is developed to control the data exchange between FDTD and SPICE. ...
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ISBN:
(纸本)078039433X
A modeling approach of incorporating SPICE into FDTD code for analysis of microwave active circuits is presented, and a set of communication agreement is developed to control the data exchange between FDTD and SPICE. With this agreement, it is very convenient for FDTD modeling of microwave circuits with random number of ports. Two typical microwave circuits are simulated by this approach as examples to illustrate the efficiency of the proposed approach.
After conventional waterflooding processes the residual oil in the reservoir remains as a discontinuous phase in the form of oil drops trapped by capillary forces and is likely to be around 70% of the original oil in ...
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After conventional waterflooding processes the residual oil in the reservoir remains as a discontinuous phase in the form of oil drops trapped by capillary forces and is likely to be around 70% of the original oil in place (OOIP). The EOR method so-called Alkaline-Surfactant-Polymer (ASP) flooding has been proved to be effective in reducing the oil residual saturation in laboratory experiments and field projects through reduction of interfacial tension and mobility ratio between oil and water phases. A critical step for the optimal design and control of ASP recovery processes is to find the relative contributions of design variables such as, slug size and chemical concentrations, in the variability of given performance measures (e.g., net present value, cumulative oil recovery), considering a heterogeneous and multiphase petroleum reservoir (sensitivity analysis). Previously reported works using reservoir numerical simulation have been limited to local sensitivity analyses because a global sensitivity analysis may require hundreds or even thousands of computationally expensive evaluations (field scale numerical simulations). To overcome this issue, a surrogate-based approach is suggested. Surrogate-based analysis/optimization makes reference to the idea of constructing an alternative fast model (surrogate) from numerical simulation data and using it for analysis/optimization purposes. This paper presents an efficient global sensitivity approach based on Sobol's method and multiple surrogates (i.e., Polynomial Regression, Kriging, Radial Base Functions and a Weighed Adaptive Model), with the multiple surrogates used to address the uncertainty in the analysis derived from plausible alternative surrogate-modeling schemes. The proposed approach was evaluated in the context of the global sensitivity analysis of a field scale Alkali-Surfactant-Polymer flooding process. The design variables and the performance measure in the ASP process were selected as slug size/concentra
A workflow management system determines the flow of work according to pre-defined business process definitions. It manages the resources required to meet goals and provides monitoring facilities and control capabiliti...
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ISBN:
(纸本)9781424416851
A workflow management system determines the flow of work according to pre-defined business process definitions. It manages the resources required to meet goals and provides monitoring facilities and control capabilities. Resources can become important decision factors when combined with control flow information. In many situations, business processes are constrained by scarce resources. The lack of resources can cause contention and the slowing down of the accomplishment of larger goals. This paper introduces a resource-constrained and decision-support workflow model, and presents a requirement analysis approach.
With the advent of the era of big data and artificial intelligence, the seamless supervision of food safety has been further promoted, and the food safety situation has been improving. When food safety meets "art...
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ISBN:
(纸本)9798400709760
With the advent of the era of big data and artificial intelligence, the seamless supervision of food safety has been further promoted, and the food safety situation has been improving. When food safety meets "artificial intelligence" and continues to be deeply integrated, it will surely become the common gospel of our people and even the whole world. This paper mainly expounds and discusses the application of artificial intelligence combined with big data in food safety and supervision. Based on this, taking a city as an example, this paper deeply analyzes the policy basis of introducing big data technology, the logical structure of big data embedding and the empowerment of big data application function in the process of food safety supervision in this city through text analysis and technology embedding theory, and hopes that this technology will be more popular in the future food safety strategy and promote the improvement of people's living standards and quality of life.
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